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We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Our…

Computer Vision and Pattern Recognition · Computer Science 2020-12-08 Pratul P. Srinivasan , Boyang Deng , Xiuming Zhang , Matthew Tancik , Ben Mildenhall , Jonathan T. Barron

Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel…

Computer Vision and Pattern Recognition · Computer Science 2018-04-10 Siyuan LI , Wenqi Ren , Jiawan Zhang , Jinke Yu , Xiaojie Guo

Conventional single image based localization methods usually fail to localize a querying image when there exist large variations between the querying image and the pre-built scene. To address this, we propose an image-set querying based…

Computer Vision and Pattern Recognition · Computer Science 2015-09-22 Lei Deng , Siyuan Huang , Yueqi Duan , Baohua Chen , Jie Zhou

Decomposing a scene into its shape, reflectance, and illumination is a challenging but important problem in computer vision and graphics. This problem is inherently more challenging when the illumination is not a single light source under…

Computer Vision and Pattern Recognition · Computer Science 2021-08-27 Mark Boss , Raphael Braun , Varun Jampani , Jonathan T. Barron , Ce Liu , Hendrik P. A. Lensch

We present a method for relighting 3D reconstructions of large room-scale environments. Existing solutions for 3D scene relighting often require solving under-determined or ill-conditioned inverse rendering problems, and are as such unable…

Computer Vision and Pattern Recognition · Computer Science 2026-01-30 Xiaoyan Xing , Philipp Henzler , Junhwa Hur , Runze Li , Jonathan T. Barron , Pratul P. Srinivasan , Dor Verbin

Recent advances have shown that large-scale video diffusion models can be repurposed as neural renderers by first decomposing videos into intrinsic scene representations and then performing forward rendering under novel illumination. While…

Computer Vision and Pattern Recognition · Computer Science 2026-05-08 Weiqing Xiao , Hong Li , Xiuyu Yang , Houyuan Chen , Wenyi Li , Tianqi Liu , Shaocong Xu , Chongjie Ye , Hao Zhao , Beibei Wang

Infrared image helps improve the perception capabilities of autonomous driving in complex weather conditions such as fog, rain, and low light. However, infrared image often suffers from low contrast, especially in non-heat-emitting targets…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Siyuan Chai , Xiaodong Guo , Tong Liu

HDR reconstruction is an important task in computer vision with many industrial needs. The traditional approaches merge multiple exposure shots to generate HDRs that correspond to the physical quantity of illuminance of the scene. However,…

Computer Vision and Pattern Recognition · Computer Science 2021-03-25 Wei Wei , Li Guan , Yue Liu , Hao Kang , Haoxiang Li , Ying Wu , Gang Hua

We introduce LightIt, a method for explicit illumination control for image generation. Recent generative methods lack lighting control, which is crucial to numerous artistic aspects of image generation such as setting the overall mood or…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Peter Kocsis , Julien Philip , Kalyan Sunkavalli , Matthias Nießner , Yannick Hold-Geoffroy

Relighting a person from a single photo is an attractive but ill-posed task, as a 2D image ambiguously entangles 3D geometry, intrinsic appearance, and illumination. Current methods either use sequential pipelines that suffer from error…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Yuxuan Xue , Ruofan Liang , Egor Zakharov , Timur Bagautdinov , Chen Cao , Giljoo Nam , Shunsuke Saito , Gerard Pons-Moll , Javier Romero

In this paper, we tackle the problem of enhancing real-world low-light images with significant noise in an unsupervised fashion. Conventional unsupervised learning-based approaches usually tackle the low-light image enhancement problem…

Image and Video Processing · Electrical Eng. & Systems 2022-03-29 Wei Xiong , Ding Liu , Xiaohui Shen , Chen Fang , Jiebo Luo

Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains on paired or unpaired low-/normal-images. However, existing methods suffer color deviation and fail to…

Computer Vision and Pattern Recognition · Computer Science 2021-07-20 Zhuqing Jiang , Haotian Li , Liangjie Liu , Aidong Men , Haiying Wang

The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces. By capturing the appearance of the human subject under different light sources, one obtains the light…

To train the change detector, bi-temporal images taken at different times in the same area are used. However, collecting labeled bi-temporal images is expensive and time consuming. To solve this problem, various unsupervised change…

Computer Vision and Pattern Recognition · Computer Science 2022-04-06 Hyeoncheol Noh , Jingi Ju , Minseok Seo , Jongchan Park , Dong-Geol Choi

Image perception is one of the most direct ways to provide contextual information about a user concerning his/her surrounding environment; hence images are a suitable proxy for contextual recommendation. We propose a novel representation…

Multimedia · Computer Science 2018-08-30 Chih-Chun Hsia , Kwei-Herng Lai , Yian Chen , Chuan-Ju Wang , Ming-Feng Tsai

High dynamic range (HDR) imaging is an indispensable technique in modern photography. Traditional methods focus on HDR reconstruction from multiple images, solving the core problems of image alignment, fusion, and tone mapping, yet having a…

Image and Video Processing · Electrical Eng. & Systems 2022-10-31 Phuoc-Hieu Le , Quynh Le , Rang Nguyen , Binh-Son Hua

Can we relocalize in a scene represented by a single reference image? Standard visual relocalization requires hundreds of images and scale calibration to build a scene-specific 3D map. In contrast, we propose Map-free Relocalization, i.e.,…

Computer Vision and Pattern Recognition · Computer Science 2022-10-12 Eduardo Arnold , Jamie Wynn , Sara Vicente , Guillermo Garcia-Hernando , Áron Monszpart , Victor Adrian Prisacariu , Daniyar Turmukhambetov , Eric Brachmann

Furnishing and rendering indoor scenes has been a long-standing task for interior design, where artists create a conceptual design for the space, build a 3D model of the space, decorate, and then perform rendering. Although the task is…

Computer Vision and Pattern Recognition · Computer Science 2022-07-26 Hong-Wing Pang , Yingshu Chen , Phuoc-Hieu Le , Binh-Son Hua , Duc Thanh Nguyen , Sai-Kit Yeung

We consider the problem of few-viewpoint 3D surface reconstruction using raw measurements from a lidar system. Lidar captures 3D scene geometry by emitting pulses of light to a target and recording the speed-of-light time delay of the…

Computer Vision and Pattern Recognition · Computer Science 2024-12-02 Weihan Luo , Anagh Malik , David B. Lindell

Separating an image into meaningful underlying components is a crucial first step for both editing and understanding images. We present a method capable of selecting the regions of a photograph exhibiting the same material as an…

Computer Vision and Pattern Recognition · Computer Science 2023-05-23 Prafull Sharma , Julien Philip , Michaël Gharbi , William T. Freeman , Fredo Durand , Valentin Deschaintre